To improve the fuel efficiency of a parallel hybrid electric racing car, this paper presents a computationally efficient energy management strategy (EMS) that regulates the internal combustion engine (ICE) to either operate with high efficiency or be switched off. Dynamic programming (DP) is employed to compute the optimal trajectories of vehicle speed and supercapacitor voltage. These trajectories are used as references for real-time online control. The online control contains two parts: a binary model predictive control (B-MPC) that determines the ICE on/off status and a Pontryagin’s minimum principle (PMP) that allocates the total torque demand to fuel and electric paths. The usual challenge to MPC is the large computational complexity and to PMP is the search of optimal costate The proposed EMS evades these two challenges. The binary property of MPC significantly reduces the computational complexity and the optimal costate of PMP is obtained from the value function computed by DP. The testing results by processor-in-the-loop simulation indicate the proposedEMS can realize a close to optimal performance and can be applied on a low-cost microprocessor.